Partner ona study.
An open invitation
Help us reveal what open scholarly knowledge includes, what it leaves out, and what can be repaired.
We welcome research partnerships, organizational invitations, and sponsorship for focused studies that produce evidence-based findings and actionable improvements.
A shared research agenda
Questions worth answering together.
Some questions can be answered quickly. Others ask why a pattern exists, what it changes, and whether an intervention works. They need different study designs.
Filter by study type
17 questions
Questions that need deeper research.
These investigate influence, causality, behavior, values, and change over time. They may require hypotheses, experiments, interviews, or longitudinal analysis.
- O01
AI recommendations
How does metadata completeness shape what AI systems recommend, summarize, cite, or leave out?
This calls for comparative observation across systems, prompts, subjects, and time. A single field check cannot answer it.
- O02
Readers and representations
Who reads research in a world mediated by AI? What should the research record become?
Investigate what people and AI systems can see, read, interpret, cite, and reuse. Ask how those actions can be observed responsibly, where PDF remains useful, and where it becomes limiting. Explore machine readable representations that retain clear provenance.
- O03
Context and trust
One record, many contexts: what should an AI agent trust across pretraining, live retrieval, and connected knowledge systems?
Compare how the same record is represented in a model’s pretraining corpus, search indexes, repositories, publisher pages, knowledge graphs, agent memory, and derived summaries. Study how provenance, version, recency, authority, and task should determine which context an agent relies on, especially when its pretraining sources cannot be inspected.
- O04
Online attention
How do online attention scores and signals influence AI recommendations? Can attention outweigh metadata quality or scholarly relevance?
Separate the influence of mentions, citations, popularity, access, and structured metadata on what an AI system surfaces.
- O05
Impact measurement
How should research impact be measured in a world mediated by AI? Do established methods still hold up?
Reconsider citations, journal metrics, downloads, online attention, policy uptake, and reuse as AI systems increasingly mediate discovery, synthesis, recommendation, and application.
- O06
Repair outcomes
When a record is repaired, does its downstream discovery, recommendation, attribution, or reuse actually improve?
Follow records over time and across platforms to test whether a technically better record produces a meaningful change.
- O07
Collective responsibility
Who is responsible for repairing research infrastructure and missing metadata? How should that responsibility be shared?
Study how responsibility, incentives, costs, and accountability should be distributed across authors, institutions, libraries, funders, publishers, repositories, infrastructure providers, aggregators, and AI platforms when metadata has not been collectively prioritized.
- O08
Meaning and governance
What should “complete” or “ready for AI” mean across fields, countries, languages, and communities? Who should decide?
Explore how a readiness framework can remain useful without treating one discipline, language, or publishing system as the universal norm.
Questions that can start with a quick audit.
These use a defined corpus and observable checks. The result can be reproduced, compared, and turned into an immediate repair list.
- V01
Fields and topics
Which disciplines, subfields, methods, and emerging topics are underrepresented, fragmented, or misclassified in open knowledge graphs?
Count and compare record and field coverage across a defined graph, classification, and time period.
- V02
Countries and regions
Which countries, regions, institutions, and research communities lose visibility between local publication and global discovery systems?
Compare source records with downstream indexes to locate measurable losses in coverage and resolution.
- V03
Languages and scripts
Whose scholarship becomes invisible when titles, abstracts, keywords, names, and affiliations are not represented across languages and scripts?
Audit the presence, script, language tags, transliterations, and translations of key descriptive fields.
- V04
Research outputs
What knowledge is missed when graphs privilege journal articles over data, software, protocols, presentations, preprints, hypotheses, and negative results?
Measure coverage by output type and whether relationships between outputs survive indexing and aggregation.
- V05
People and contribution
How often are contributors, affiliations, funders, facilities, communities, and roles beyond authorship missing or unresolved?
Check the presence, identifier resolution, and consistency of attribution fields across a defined corpus.
- V06
Connections and context
Which links between articles, data, code, methods, grants, corrections, and later versions are present for people but absent for machines?
Test whether declared relationships are structured, reciprocal, resolvable, and preserved downstream.
- V07
Beyond Crossref
Which parts of the Nexus Index framework transfer beyond Crossref, and what additional gaps become measurable when other scholarly infrastructures are included?
Map the five facets against DataCite, OpenAlex, ORCID, ROR, institutional repositories, CRIS platforms, funder systems, and domain indexes. Test field availability, identifier resolution, relationship preservation, and overlap with Crossref to identify portable checks, necessary adaptations, and new opportunities across datasets, software, preprints, grants, affiliations, versions, and agent facing access.
- V08
Access and reuse
Which records appear open but remain difficult to reuse because licences, rights, access routes, formats, or versions cannot be resolved by machines?
Run repeatable checks for licence metadata, access endpoints, content types, version markers, and working links.
- V09
Baseline machine readiness
Can a machine resolve the identifier, retrieve the described object, distinguish its version, trace its provenance, and find its reuse conditions?
Apply a transparent checklist to produce a fast, reproducible readiness baseline and a concrete repair list.
Ways to participate
Partner with us!
Partnerships can begin with expertise, data, access, funding, a publication opportunity, or a problem your organization cannot answer alone.
- 01
Design a study together
Bring a research question, field, region, language, or community. We shape a bounded study and interpretation plan together.
- 02
Contribute a corpus
Provide a lawful dataset, catalogue, knowledge graph, repository sample, or metadata pipeline that can support a meaningful investigation.
- 03
Sponsor a study
Fund data access, analysis, infrastructure, community participation, or open publication. Sponsorship supports the work. It does not determine the conclusions.
- 04
Publish and convene
Help us share and test our findings – in a journal, at a conference, workshop, or other community venue
Invitations welcome
Organizations across the knowledge ecosystem can take part.
Work that becomes a public record
Study carefully. Publish usefully.
The intended result is publishable research with transparent scope, methods, limitations, attribution, and actionable findings. Where rights and safeguards allow, outputs may include papers, preprints, reports, code, and reusable derived data.
- 01Frame the question and affected community
- 02Agree methods, responsibilities, and safeguards
- 03Measure gaps and test actionable repairs
- 04Publish findings, methods, and reusable outputs
Start the conversation
What should open scholarly knowledge reveal next?
Tell us the question, affected community, relevant corpus or infrastructure, and how you would like to partner or sponsor the work.